The catastrophic failure of machines in industries such as the power generation industry and manufacturing/process industries is an ongoing problem. Machines such as gas turbines, motors, pumps and compressors are susceptible to failures that may cause unexpected, extremely expensive plant or line shutdowns. To prevent such failures, industries have implemented preventive maintenance programs to insure that key components such as bearings, turbine blades and lubrication systems are inspected or replaced on a regular basis. Such scheduled maintenance programs, however, must balance the high cost of a maintenance event, often itself requiring shut-down, with the possibility that a component will fail prematurely, before it is replaced. The result is often the performance of unnecessary maintenance, whether needed or not, under the belief that performing the unnecessary maintenance is preferable to an increased risk of a component failure.
Remote machine monitoring and early fault detection have been used to more intelligently schedule machine maintenance. For example, condition-based maintenance (CBM) uses measured information about a machine to determine the health of the machine, and maintenance is performed only when actually necessary. Predictive maintenance (PM) systems perform maintenance at a point in time when the maintenance is most cost effective, but before the equipment performs less than optimally. Both systems reduce maintenance costs and decrease plant downtimes; thereby increasing operating revenue.
While CBM and PM systems do indeed reduce downtime, there is a need to improve the ability of such systems to accurately assess the condition of a given machine and reliably predict the need for maintenance. Any such improvement reduces the frequency of maintenance and thereby reduces maintenance costs.